Sentence examples for our data objective from inspiring English sources

In our data, objective AoA is no less correlated with other variables than rated AoA as suggested by some studies [9], [14], [15].

Choosing fixed time intervals from admission to the PACU, and probably adding repeated assessments would have made our data more objective and reproducible.

21 Our data confirmed that the objective duration of the surgery was longer in cases with worse preoperative visual acuity, as more advanced cataracts require a longer duration of ultrasonic power release.

And without access to meaningful data, objective analyses cannot be developed by academics, consumer advocates or the media.

In order to make the data objective and comparable, we presented it in relation to rpks.

Differences in automatically generated behavioural data (objective data) collected from the smartphones (e.g. mobility, social and physical activity etc).

As such, our data reveal common, objective neural markers for music aptitude and reading ability and suggest a model for the relationships that have been documented between music and literacy performance [ 28- 31, 53].

To examine the tempo and mode of diversification of Sigmodontinae we used R version 3.0.1 [ 35] in combination with the packages ape [ 36], diversitree [ 37], GEIGER [ 38], LASER [ 39], phytools [ 40], TreePar [ 41] and TreeSim [ 42].To correct for any sampling bias in our data, we applied an objective automated method for fitting diversification models to non-randomly sampled phylogenies.

Although the optimal sequencing depth depends on the study objectives, our data suggest that a sequencing depth of at least 10× would be necessary to discover genome-wide SNPs for use in haplotype analysis (additional file 2).

We of course cannot fully rule out the possibility that there are some other models that can better explain our choice data using objective hazard rates.

However, it proved to be impossible with our data from living subjects: the objective function was difficult to optimize and subject to very large estimation errors.